2. Exploratory data analysis
##2.1 Data structure and summary
#> 'data.frame': 1000 obs. of 32 variables:
#> $ OBS. : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ CHK_ACCT : Factor w/ 4 levels "0","1","2","3": 1 2 4 1 1 ..
#> $ DURATION : int 6 48 12 42 24 36 24 36 12 30 ...
#> $ HISTORY : Factor w/ 5 levels "0","1","2","3",..: 5 3 5 3..
#> $ NEW_CAR : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 ..
#> $ USED_CAR : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 ..
#> $ FURNITURE : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 2 1 1 ..
#> $ RADIO.TV : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 2 ..
#> $ EDUCATION : Factor w/ 3 levels "-1","0","1": 2 2 3 2 2 3 2..
#> $ RETRAINING : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 ..
#> $ AMOUNT : int 1169 5951 2096 7882 4870 9055 2835 6948 3..
#> $ SAV_ACCT : Factor w/ 5 levels "0","1","2","3",..: 5 1 1 1..
#> $ EMPLOYMENT : Factor w/ 5 levels "0","1","2","3",..: 5 3 4 4..
#> $ INSTALL_RATE : int 4 2 2 2 3 2 3 2 2 4 ...
#> $ MALE_DIV : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 ..
#> $ MALE_SINGLE : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 2 2 1 ..
#> $ MALE_MAR_or_WID : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 ..
#> $ CO.APPLICANT : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 ..
#> $ GUARANTOR : Factor w/ 3 levels "0","1","2": 1 1 1 2 1 1 1 ..
#> $ PRESENT_RESIDENT: Factor w/ 4 levels "1","2","3","4": 4 2 3 4 4 ..
#> $ REAL_ESTATE : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 2 ..
#> $ PROP_UNKN_NONE : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 1 1 1 ..
#> $ AGE : int 67 22 49 45 53 35 53 35 61 28 ...
#> $ OTHER_INSTALL : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 ..
#> $ RENT : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 ..
#> $ OWN_RES : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 2 1 2 ..
#> $ NUM_CREDITS : int 2 1 1 1 2 1 1 1 1 2 ...
#> $ JOB : Factor w/ 4 levels "0","1","2","3": 3 3 2 3 3 ..
#> $ NUM_DEPENDENTS : int 1 1 2 2 2 2 1 1 1 1 ...
#> $ TELEPHONE : Factor w/ 2 levels "0","1": 2 1 1 1 1 2 1 2 1 ..
#> $ FOREIGN : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 ..
#> $ RESPONSE : Factor w/ 2 levels "0","1": 2 1 2 2 1 2 2 2 2 ..
#> OBS. CHK_ACCT DURATION HISTORY NEW_CAR USED_CAR
#> Min. : 1 0:274 Min. : 4.0 0: 40 0:766 0:897
#> 1st Qu.: 251 1:269 1st Qu.:12.0 1: 49 1:234 1:103
#> Median : 500 2: 63 Median :18.0 2:530
#> Mean : 500 3:394 Mean :20.9 3: 88
#> 3rd Qu.: 750 3rd Qu.:24.0 4:293
#> Max. :1000 Max. :72.0
#> FURNITURE RADIO.TV EDUCATION RETRAINING AMOUNT SAV_ACCT
#> 0:819 0:720 -1: 1 0:903 Min. : 250 0:603
#> 1:181 1:280 0 :950 1: 97 1st Qu.: 1366 1:103
#> 1 : 49 Median : 2320 2: 63
#> Mean : 3271 3: 48
#> 3rd Qu.: 3972 4:183
#> Max. :18424
#> EMPLOYMENT INSTALL_RATE MALE_DIV MALE_SINGLE MALE_MAR_or_WID
#> 0: 62 Min. :1.00 0:950 0:452 0:908
#> 1:172 1st Qu.:2.00 1: 50 1:548 1: 92
#> 2:339 Median :3.00
#> 3:174 Mean :2.97
#> 4:253 3rd Qu.:4.00
#> Max. :4.00
#> CO.APPLICANT GUARANTOR PRESENT_RESIDENT REAL_ESTATE PROP_UNKN_NONE
#> 0:959 0:948 1:130 0:718 0:846
#> 1: 41 1: 51 2:308 1:282 1:154
#> 2: 1 3:149
#> 4:413
#>
#>
#> AGE OTHER_INSTALL RENT OWN_RES NUM_CREDITS
#> Min. : 19.0 0:814 0:821 0:287 Min. :1.00
#> 1st Qu.: 27.0 1:186 1:179 1:713 1st Qu.:1.00
#> Median : 33.0 Median :1.00
#> Mean : 35.6 Mean :1.41
#> 3rd Qu.: 42.0 3rd Qu.:2.00
#> Max. :125.0 Max. :4.00
#> JOB NUM_DEPENDENTS TELEPHONE FOREIGN RESPONSE
#> 0: 22 Min. :1.00 0:596 0:963 0:300
#> 1:200 1st Qu.:1.00 1:404 1: 37 1:700
#> 2:630 Median :1.00
#> 3:148 Mean :1.16
#> 3rd Qu.:1.00
#> Max. :2.00
##2.2 Graphs for each variable
###2.2.1 Graph for response

###2.2.2 Graphs for all numeric variables

###2.2.3 Graphs for the purpose of credit variables

###2.2.4 Graphs for other categorical variables
###2.2.4 Variables in response
###2.2.5 Correlation plot
